Intelligent fault classification of a tractor starter motor using vibration monitoring and adaptive neuro-fuzzy inference system

Abstract

This paper presents an intelligent method for fault diagnosis of the starter motor of an agricultural tractor, based on vibration signals and an Adaptive Neuro-Fuzzy Inference System (ANFIS). The starter motor conditions to be considered were healthy, crack in rotor body, unbalancing in driven shaft and wear in bearing. Thirty-three statistical parameters of vibration signals in the time and frequency domains were selected as a feature source for fault diagnosis. A data mining filtering method was performed in order to extract the superior features among the primary thirtythree features for the classification process and to reduce the dimension of features. In this study, six superior features were fed into an adaptive neuro-fuzzy inference system as input vectors. Performance of the system was validated by applying the testing data set to the trained ANFIS model. According to the result, total classification accuracy was 86.67%. This shows that the system has great potential to serve as an intelligent fault diagnosis system in real applications.

Spark plug fault recognition based on sensor fusion and classifier combination using Dempster–Shafer evidence theory

“…In another research, a number of the features have been employed for fault diagnosis of low speed bearing by Widodo et al [29]. Ebrahimi and Mollazade [30] and Khazaee et al [31] have used some of these features for fault diagnosis of tractor starter motor and planetary gearbox, respectively. Devasenapati et al [32] have employed a number of the features for misfire identification in a petrol engine.…”

An Appropriate Procedure for Detection of Journal-Bearing Fault using Power Spectral Density, K-Nearest Neighbor and Support Vector Machine

“…In this work, we studied the accuracy rate of the KNN and SVM. The classifiers were trained by training data set, and then their performance was exactly estimated by testing data set [30]. Also, their accuracy rate was compared.…”

“…The accuracy of feature extraction is of great importance since it directly affects the final diagnosis results [5]. In this work, 30 …”

Fault diagnosis and classification of water pump using adaptive neuro-fuzzy inference system based on vibration signals

“…15 In other words, defuzzification process is occurred in this layer and the outputs of layer 4 are aggregated. Therefore, the single output of the ANFIS is given by…”